NDLI: Random forests ensemble classifier trained with data resampling strategy to improve cardiac arrhythmia diagnosis.

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Random forests ensemble classifier trained with data resampling strategy to improve cardiac arrhythmia diagnosis.

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Random forests ensemble classifier trained with data resampling strategy to improve cardiac arrhythmia diagnosis.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Ozçift, Akin
Description	Country affiliation: Turkey Author Affiliation: Ozçift A ( Gaziantep Vocational School, Computer Programming Division, University of Gaziantep, Gaziantep, Turkey. akinozcift@hotmail.com)
Abstract	Supervised classification algorithms are commonly used in the designing of computer-aided diagnosis systems. In this study, we present a resampling strategy based Random Forests (RF) ensemble classifier to improve diagnosis of cardiac arrhythmia. Random forests is an ensemble classifier that consists of many decision trees and outputs the class that is the mode of the class's output by individual trees. In this way, an RF ensemble classifier performs better than a single tree from classification performance point of view. In general, multiclass datasets having unbalanced distribution of sample sizes are difficult to analyze in terms of class discrimination. Cardiac arrhythmia is such a dataset that has multiple classes with small sample sizes and it is therefore adequate to test our resampling based training strategy. The dataset contains 452 samples in fourteen types of arrhythmias and eleven of these classes have sample sizes less than 15. Our diagnosis strategy consists of two parts: (i) a correlation based feature selection algorithm is used to select relevant features from cardiac arrhythmia dataset. (ii) RF machine learning algorithm is used to evaluate the performance of selected features with and without simple random sampling to evaluate the efficiency of proposed training strategy. The resultant accuracy of the classifier is found to be 90.0% and this is a quite high diagnosis performance for cardiac arrhythmia. Furthermore, three case studies, i.e., thyroid, cardiotocography and audiology, are used to benchmark the effectiveness of the proposed method. The results of experiments demonstrated the efficiency of random sampling strategy in training RF ensemble classification algorithm.
File Format	HTM / HTML
ISSN	00104825
Issue Number	5
Volume Number	41
e-ISSN	18790534
Journal	Computers in Biology and Medicine
Language	English
Publisher	Elsevier
Publisher Date	2011-05-01
Publisher Place	United States
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Discipline Biology Discipline Biomedical Informatics Arrhythmias, Cardiac Diagnosis Computational Biology Methods Algorithms Classification Artificial Intelligence Databases, Factual False Positive Reactions Humans Roc Curve Regression Analysis Reproducibility Of Results Sample Size Software Journal Article
Content Type	Text
Resource Type	Article
Subject	Health Informatics Computer Science Applications

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